(2009) that limiting the number of word pairs decreases the performance.

we compared the top 100, 250, 500, 1000, 2500, 5000 word pairs ranked by Information Gain, non-zero Information Gain word pairs and non-filtered word pairs.


However, from the entire system point of view, the current implementation is still preliminary.

the confirmation and clarification mechanisms are isolated, for which the surface realisations sometimes may sound stiff.


In this paper, we propose a novel constituent-based approach to argument labeling, which integrates the advantages of both linear tagging and subtree extraction.

the proposed approach unifies intra-and intersentence cases by treating the immediately preceding sentence as a special constituent.


In this paper, we focus on argument labeling in the PDTB corpus.

we propose a novel constituent-based approach to argument labeling which views constituents as candidate arguments.


As the first large-scale annotated corpus that follows the lexically grounded, predicate-argument approach in D-LTAG (Lexicalized Tree Adjoining Grammar for Discourse) (Webber, 2004), the PDTB regards a connective as the predicate of a discourse relation which takes exactly two text spans as its arguments.

the text span that the connective is syntactically attached to is called Arg2, and the other is called Arg1.


In this paper, a joint inference mechanism is introduced to incorporate various kinds of knowledge to resolve the inconsistencies in argument classification to ensure global legitimate predictions.

the joint inference mechanism is formalized as a constrained optimization problem, represented as an integer linear programming (ILP) task.


2 Then this constraint ensures that at most one of the constituents can be assigned as Arg1 or Arg2.

at least k − 1 constituents should be assigned the special label NULL.


Constraint 5: The number of discontinuous constituents assigned as Arg1 or Arg2 should be at most 2.

if argument candidates c 1 , c 2 ..., c k corresponding to the same connective are discontinuous, this constraint ensures that at most two of the constituents can be assigned the same label Arg1 or Arg2.


In principle, constituents spanning the same sequence of words should be viewed as the same candidate.

for different candidates, we can unify them by adding phantom candidates.


For a phantom candidate, we set the same prior distribution as the confidence score.

the probability of the "NULL" class is set to 0.55, following (Punyakanok et al., 2008), and the probabilities of Arg1 and Arg2 are set to their occurrence frequencies in the training data.


In this paper, we focus on the problem of automatically labeling the full argument spans of discourse connectives.

we propose a constituent-based approach to integrate the advantages of both subtree extraction and linear tagging approaches.


In our pilot study we found that it's beneficial to force a cross-reference or emphasis entity as a word when the item has 2 or 3 characters.

if an entity in the Wikipedia has three characters it receives the labels of "BIE"; and if it has two characters it is labelled as "BE".


We, for the first time, present a comprehensive empirical study of Japanese word segmentation and POS tagging on microblog texts by using this corpus.

we investigated how well current models trained on existing corpora perform in the microblog domain.


3 • We propose a joint model with lexical normalization for better handling of orthographic diversity in microblog texts.

we present a new method of training the joint model using a partially annotated corpus (c.f., Section 7.4).


Although this paper targets only informal phonological variations (c.f., Section 3), other types of ill-spelled words were also annotated to investigate their frequency distribution in microblog texts.

besides informal phonological variations, spelling errors and Twitter-specific abbreviations were annotated.


This means that punctuation prediction and disfluency prediction do influence each other, and the prediction outcome in one task can provide useful information that helps to improve the other task.

we compare the cascade models and the joint prediction models.


To learn model parameter W and U, we apply a ranking scheme on candidates selected from phrase table results of each focused phrase.

given a focus phrase w, aligned phrase is treated as positive label whereas phrases extracted from other candidates in phrase table are treated as negative label.


We focus on the posterior regularization framework and improve upon the previous work by proposing new constraint functions that take into account the difference in languages in terms of content words and function words.

we differentiate between content words and function words by frequency in bilingual data, following Setiawan et al.


In order to solve the problem, we improve Ganchev's symmetric constraint so that it can consider the difference between content words and function words in each language.

we follow the frequency-based idea of Setiawan et al.


Following Bisazza and Federico (2012), we automatically decide the threshold for word frequency to discriminate between content words and function words.

the threshold is determined by the ratio of highly frequent words.


We perform word alignment using IBM model 4 (Brown et al., 1993), and then extract phrase pairs according to the alignment and automaticallyannotated target syntax.

consistent (Och et al., 1999) and cohesive (Fox, 2002) phrase pairs are extracted from intersected alignments in both directions: the target side must form a projective span, with a single root, and the source side must be contiguous.


So we used the probability score as a feature in the decoding.

we did not use classification results directly but use the probability score for softly constraining the decoding process.


The main difference between our proposed method in this paper and our previous approach is that n-grams outside the corpus are generated firstly and the probabilities using CSLM are calculated by using the same method as our previous approach.

the proposed new method is the same as our previous one when no grown n-grams are generated.


Since it is their peculiar linguistic nature that makes those expressions problematic for SMT, it is our thesis that providing more linguistic information to the translation process will improve it.

we concentrate on a specific type of MWEs, namely phrasal verbs (PVs).


We can further enrich the representation φ K , if we consider all the information available to the human judges when they are ranking translations.

the two alternative translations along with their corresponding reference.


That is, the two alternative translations along with their corresponding reference.

let r and r be the references for the pairs t 1 , t 2 and t 1 , t 2 , we can redefine all the members of Eq.


There is considerable evidence that understanding the semantic properties of words improves child vocabulary acquisition.

children are sensitive to commonalities of semantic categories, and this abstract knowledge facilitates subsequent word learning (Jones et al., 1991;Colunga and Smith, 2005).


Steyvers and Tenenbaum (2005) demonstrated that a semantic network that encodes adult-level knowledge of words exhibits a small-world and scale-free structure.

it is an overall sparse network with highly-connected local sub-networks, where these sub-networks are connected through high-degree hubs (nodes with many neighbours).


(2010) examine different categorization models based on RMC.

they compare the performance of the approximation algorithm of Anderson and Matessa (1992) (local MAP) with two other approximation algorithms (Gibbs Sampling and Particle Filters) in various human categorization paradigms.


Our proposal is instead to consider for S those words that are Algorithm 1 Growing a network after each input u. for all w in u do update P (.|w) using Eqn.

(2) update current connections of w select S(w), a subset of words in the network for all w in S(w) do if w and w are sufficiently similar then connect w and w with an edge end if end for end for likely to be similar to w. since the network only needs to connect similar words to w, if we can guess what (some of) those words are, then we will do best at approximating the situation of comparing w to all words.


To do so, we incrementally track semantic similarity among words usages as their meanings are developing.

we cluster word tokens (not types) according to their current word meanings.


As in much previous work (Yu and Ballard, 2007;Fazly et al., 2010), we take child-directed utterances from the CHILDES database (MacWhinney, 2000) in order to have naturalistic data.

we use the Manchester corpus (Theakston et al., 2001), which consists of transcripts of conversations with 12 British children between the ages of 1; 8 and 3; 0.


The model adheres to the cognitive plausibility requirements of incrementality and use of limited computations.

when incrementally adding or updating a word's connections in the network, the model only looks at a subset of words rather than comparing the target word to all the nodes in the network.


Our basic feature representation is inspired from the feature representation of the MST parser (McDonald et al., 2005) except that in the parser the features represent a directed edge in the complete directed graph defined over the words in a sentence that is to be parsed, while our features are generated for word n-grams.

our feature set is a concatenation of two sets derived from the MST set described in Table 1 of (McDonald et al., 2005) in the following way: (1) In both sets the parent word in the parser's set is replaced with the represented word; (2) In one set every child word in the parser's set is replaced by the word to the left of the represented word and in the other set it is replaced by the word to its right.


The low K-value tree (K=2) is shown in 3a, and primarily suffers from issues related to the prepositional phrase, 'in Europe friday'.

the low K-value tree incorrectly groups 'Europe friday' as a noun phrase object of 'in'.


The directionality of the edges is based on the sequence of words in the text messages in the corpus.

an edge between two nodes is directed from the earlier seen token towards the later seen token in a message.


Our graph-based contextual similarity method is based on the assumption that an IV word that is the canonical form of an OOV word appears in the same context with the corresponding OOV word.

the two nodes in the graph share several neighbors that co-occur within the same distances to the corresponding two words in social media text.


While extending the candidate set with lexical features we use t edit ≤ 2 ∨ t phonetic ≤ 1 to keep up with the settings in (Han and Baldwin, 2011).

iV words that are within 2 character edit distance or 1 character edit distance of a given OOV word under phonemic transcription were chosen as lexical similarity candidates.


However, this approach is led by statistical information.

it finds which IV word the OOV word is commonly normalized to, regardless of the context of the OOV word in the input text message.


In addition to broadening the scope of query answering, extending the coverage of the knowledge base to long tail attributes can also facilitate providing Web answers to the user.

the search engine can use lower-confidence facts to corroborate an answer that appears in text in one of the top Web results and highlight them to the user.


However, the extraction rules and the application of these rules are tailored to our task of extracting noun-based attributes.

when we apply an extraction rule to generate a triple (S,A,O), we require that (1) A is an attribute in our ontology, and (2) the value of A and the object O corefer to the same real-world Figure 1: Extraction Pipeline: we begin with a set of high-precision extractors and use distant supervision to train other extractors.


Moving from larger to smaller Wikipedias, FlatLR becomes more competitive.

flatLR outperforms NB and is close to HierLR for PTWIKI14, the smallest of the three (and significantly smaller than TWUS).


The intuition is that the context helps to disambiguate the true meaning of the occurrence of the word w S 1 .

after observing the context of the word w S 1 , fewer latent cross-lingual concepts will share most of the probability mass in the modulated context-aware word representation.


The idea now is to smooth the representation of a word in the latent semantic space induced only by the words in its local context with the out-of-context type-based representation of that word induced directly from a large training corpus.

the modulated probability score P (z k |w S 1 ) from eq.


The procedure ensures that only the most semantically similar words in a given scope (e.g., a sentence) influence the choice of a correct meaning.

closely semantically similar words in the same sentence are more reliable indicators for the most probable word meaning.


Motivated by the above observations, we attempt to jointly model the shared arguments' roles.

we utilize the discriminative reranking approach that has been successfully employed in many NLP tasks.


We investigate the effect of multiple predicates in Argument Identification and Argument Classification respectively.

in the stage of Argument Identification, we introduce new features related to predicates which are effective to recognize many argument identification errors.


WordNet provides an important resource for obtaining the set of clause types that are compatible with each sense of a verb.

each verb sense in WordNet is annotated with a set of frames (e.g., "somebody verb something") in which they may occur, capturing both syntactic and semantic constraints.


We use three different methods to construct the repository.

we harness the senseannotated WordNet glosses 4 as well as the senseannotated SemCor corpus (Landes et al., 1998).


Since sentences are often more specific than glosses, and thus less helpful for constructing our repository, we generalized the so-found object senses using a heuristic method.

we first obtained all the object senses of each verb sense, and then repeatedly generalized sets of at least two senses that share a direct hypernym to this hypernym.


Second, we introduce the use of vector space similarity in random walk inference in order to reduce the sparsity of surface forms.

when following a sequence of edge types in a random walk on a graph, we allow the walk to follow edges that are semantically similar to the given edge types, as defined by some vector space embedding of the edge types.


To compute feature values, PRA begins at some node and follows edges of type π i until the sequence is finished and a target node has been reached.

if a random walk is at a node n with m outgoing edge types {e 1 , e 2 , .


This is because MAP is computed as the precision of all possible correct predictions in a ranked list, where precision is counted as 0 if the correct prediction is not included in the list.

there are many relation instances in our randomly selected test set that are not inferrable from the knowledge base, and the low recall hurts the MAP metric.


Statistical significance of the Naive-Bayes+Rules system has been marked with a † for the Baseline lemma and with a * for the Baseline wsd FACTOTUM class with respect to the baseline.

the accuracy of the hybrid system on this class is 79% while that of the baseline is only 65%.


Overall, SCDM covers 53.71The main difference with respect to (Caselli et al., 2014) is that the proposed alignments have been additionally filtered on the basis of the output of the WN domain system (NaiveBayes wsd +Rules).

for each aligned pair which was considered as good in (Caselli et al., 2014), we have applied a further filtering based on the WN domain system results as follows: if two senses are aligned but do not have the same domain, they are excluded from the WSA results, otherwise they are retained.


To construct a distributional thesaurus, the (collocational or syntactic) contexts in which a target word occurs are used as the basis for calculating its similarity with other words.

two words are similar if they share a large proportion of contexts.


(2007), that computes the distribution over head-label pairs for a given modifier, Pr(h, l | x, m).

for each modifier token we only consider the dependencies or heads whose probability is above a factor γ of the most likely dependency for the given modifier.


The model uses multipleinstance regression (MIR), based on the assumption that not all the parts of a text have the same contribution to the prediction of the rating.

a text is seen as a bag of sentences (instances), each of them modeled as a word vector.


We address this issue by developing a joint segmentation and classification framework (JSC), which simultaneously conducts sentence segmentation and sentence-level sentiment classification.

we use a log-linear model to score each segmentation candidate, and exploit the phrasal information of top-ranked segmentations as features to build the sentiment classifier.


Examples are considered to be a reliable negative only if both techniques agree that they are negative.

(Blei et al., 2003), and assumes that each document can be represented as the distribution of several topics, each document is associated with common topics.


Individual property is taken into account to measure the relationship between every spy example and all representative ones.

for example x, we firstly compute its similarity to each of the representative examples, and then the probability of the example x belonging to the positive class and negative class can be calculated using the following formula: In the above formula, A scheme mixing population and individual property is designed to generate the similarity weights of spy examples.


Specifically, for example x, we firstly compute its similarity to each of the representative examples, and then the probability of the example x belonging to the positive class and negative class can be calculated using the following formula: In the above formula, A scheme mixing population and individual property is designed to generate the similarity weights of spy examples.

for spy example x, their similarity weights can be obtained by the following formula: Where λ is a parameter to balance the information from population property and individual property.


A few frequently occurring ones are: problem, notion, concept, issue, fact, belief, decision, point, idea, event, possibility, reason, trouble, question, plan, theory, aim, and principle.

defining the notion of shell-nounhood is tricky.


Schmid exploits these lexico-syntactic constructions to identify shell noun usages.

he provides a number of typical lexico-syntactic patterns that are indicative of either anaphoric or cataphoric shell noun occurrences.


The idea of these semantic frames is similar to that of frames in Frame semantics (Fillmore, 1985) and in semantics of grammar (Talmy, 2000).

schmid follows Talmy's conception of frames.


Include Schmid's cues (+SC) This version exploits the knowledge encoded in Schmid's semantic families (Section 4.2), and extracts postnominal clauses only if Schmid's pattern cues are satisfied.

given a shell noun, we determine the families in which it occurs and list all possible patterns of these families as shell content cues.


The A+SC results for the shell nouns idea, issue, concept, decision, reason, and fact outperform the baseline and the A-SC results.

the A+SC results for the shell nouns fact and reason are markedly better than the baseline results.


Although the A-SC results for the shell nouns plan, policy, problem, trouble, difficulty, and phenomenon are well above the baseline, the A+SC results are markedly below it.


This is a first step towards end-to-end shell noun resolution.

this method can be used to create training data for any given shell noun, which can later be used to resolve harder anaphoric cases of that noun using the method that we proposed earlier (Kolhatkar et al., 2013b).


For example, the attribute black has not been explicitly mentioned for the kitten in the second sentence.

aRIS updates an implicit attribute using the previously observed attribute.


Table 3 also shows that the sentence categorization is performed with high accuracy even if the problem types and also the verbs are different.

there are a total of 118 verbs among which 64 verbs belong to MA datasets and 54 are new to IXL.


(2014)) on solving addition and subtraction problems with diversity between the training and test sets.

our method generalizes better to data from different domains because ARIS only relies on learning verb categories which alleviates the need for equation templates for arithmetic problems.


We propose three different models for term translation that attempt to address the three issues mentioned above.

• Term Translation Disambiguation Model: In this model, we condition the translations of terms in different documents on corresponding per-document topic distributions.


In this paper, we adopt the first strategy.

for each sentence pair, we collect all source phrases which are terms and find aligned target phrases for them via word alignments.


Otherwise, we may need to create a new translation for it according to its context.

we want to enable the decoder to choose between: 1) translating a given source term into the extracted corresponding target term or 2) translating it in another way according to the strength of its translation consistency.


While it is true that the same could be achieved by an explicit enumeration, lattice rules make this easier and more efficient.

we show that a decoding algorithm working with such lattice rules can be more efficient than one working directly on the enumeration of the rules encoded in the lattice.


in the speed/memory performances of the decoding.

we propose a method to merge and reduce the size of the lattice rules and show that it translates into better performances at decoding time.


In both situation, it seems like it would be better to delay the full specification of the rule until decoding time, when the decoder can have access to the surrounding context of the rule and make a more informed choice.

we can expect features such as language model or governor-dependent features (in the case of tree-to-tree Machine translation) to help remove the ambiguities.


Finally, we evaluate the advantages of extracting lattice rules such as proposed in section 3.

we consider rules for which null-aligned words are bypassable by epsilonedges, for which Non-terminal are allowed to take several alternative positions around the word that is thought to be their governor, and for which we consider alternative morphologies of a few words ("is/are", "a/an").


Moreover, even without trained classifiers, this model can be used to estimate potential benefits by plugging in hypothetical error values.

one can ask the question "If I had classifiers with x% error on this query set, what would be the benefit of using these classifiers to combine QT methods?"


This disequilibrium is induced by the multilingual concept coverage of Ba-belNet, as stated by its authors (Navigli and Ponzetto, 2012a), (Navigli and Ponzetto, 2012b).

the WSD process tightly depends from the concept coverage supplied from the language-specific knowledge base.


The β ratio can explain the discrep- ancy in (language-specific) performances in the two scenarios.

the difference in the β statistic between English and the other languages is more evident for the Unbalanced Corpus (i.e., 0.23 between English and French), while it is lower for the Balanced Corpus (around 0.1).


BoS produces a smaller representation space over which documents are projected, but it is enough rich to well capture the documents content.

boS benefits from the WSD process that is able to discriminate the same term w.r.t.


We encode both the path representation from (ii), and the entire subgraph of entities connected to the candidate answer entity.

for each entity connected to the answer we include both the relation type and the entity itself in the representation ψ(a).


Instead, we consider the following general approach: given that we are predicting a path, we can predict its elements in turn using Method P@1 F1 F1 (%) (Berant) (Yao) Baselines (Berant et al., 2013) -31.4 - (Bordes et al., 2014b) 31  a beam search, and hence avoid scoring all candidates.

our model first ranks relation types using Eq.


ir-wiki performs better than other models because Wikipedia contains many more sentences that partially match specific words or phrases found in early clues than the question training set.

it is impossible for all other models to answer clues in the test set that have no semantically similar or equivalent analogues in the training question data.


As common in previous work (Yan et al., 2013;He et al., 2012) we assume that our generator operates in an interactive context.

the user supplies keywords (e.g., spring, lute, drunk ) highlighting the main concepts around which the poem will revolve.


We compress two neighboring vectors in the first two layers and three neighboring vectors in the remaining layers.

for quatrains with seven characters, we use C 1,2 , C 2,2 , C 3,3 , C 4,3 to merge vectors in each layer (see Figure 3); and for quatrains with five characters we use C 1,2 , C 2,2 , C 3,3 .


Summarization Data For summarization experiments, we use the standard TAC data sets 1 , which have been used in the NIST competitions.

we used the TAC 2010 data set as training data for the SVR sentence pre-selection model, TAC 2009 data set as development set for parameter tuning, and the TAC 2008 and 2011 data as the test set for reporting the final summarization results.


The CRF-based compression model used in (Li et al., 2013a) can not well model the grammar.

our results (ROUGE-2) are statistically significantly (p < 0.05) higher than TAC08 Best system, but are not statistically significant compared with (Li et al., 2013a)  The pattern is similar in TAC 2011 data.


Due to this significant difference, we can hypothesize that the documents about the same topic show a more homogeneous structure.

a Deriv form is usually seen in the same surface form in a cluster of documents which are about the same topic.


(2012), Kalchbrenner and Blunsom (2013) and many others).

this is the first large-scale study to date that applies neural word representations in tensor-based compositional distributional models of meaning similar to those formalized by Coecke et al.


We test a range of implementations based on this framework, together with additive and multiplicative approaches (Mitchell and Lapata, 2008), in a variety of different tasks.

we use the verb disambiguation task of Grefenstette and Sadrzadeh (2011a) and the transitive sentence similarity task of Kartsaklis and Sadrzadeh (2014) as small-scale focused experiments on pre-defined sentence structures.


Assume that the probability of a specific (c, t) pair of words (where t is a target word and c another word in the same context with t), coming from the training data, is denoted as p (D = 1|c, t).

the objective function is then expressed as follows: the goal is to set the model parameters in a way that maximizes the probability of all observations coming from the training data.


The objective function is then expressed as follows: That is, the goal is to set the model parameters in a way that maximizes the probability of all observations coming from the training data.

assume now that D is a set of randomly selected incorrect (c , t ) pairs that do not occur in D, then Equation 2 above can be recasted in the following way: (3) the model tries to distinguish a target word t from random draws that come from a noise distribution.


There is no statistically significant difference between KS14 addition and NWE addition (the second best result).

we get classification results on the Microsoft Research Paraphrase Corpus paraphrase corpus (Dolan et al., 2005) working in the following way: we construct vectors for the sentences of each pair; if the cosine similarity between the two sentence vectors exceeds a certain threshold, the pair is classified as a paraphrase, otherwise as not a paraphrase.


More recent work relaxes the Markovian assumption of CRFs to capture phrase-level interactions, significantly improving upon the token-level labeling approach (Yang and Cardie, 2012).

yang and Cardie (2013) propose a joint inference model to jointly detect opinion expressions, opinion holders and targets, as well as the relations among them, outperforming previous pipelined approaches.


We also include versions of the baselines that have access to pre-trained word vectors.

cRF+VEc employs word vectors as continuous features per every token.


To apply the IORNN architecture to dependency parses we need to adapt the definitions somewhat.

in the generative dependency model, every step in the generative story involves the decision to generate a specific word while the span of the subtree that this word will dominate only becomes clear when all dependents are generated.


(2011)), the POS-tags and capitalisation features.

the inner representation at a node h is given by: Training Training this IORNN is to minimise the following objective function which is the regularised cross-entropy where D is the set of training dependency parses, m is the number of dependents; θ W , θ L are the weight matrix set and the word embeddings Implementation We decompose a dependent D into four features: dependency relation, POS-tag, lowercase version of word, capitalisation feature of word.


In recent years, enormous parsing success has been achieved by the use of feature-based discriminative dependency parsers (Kübler et al., 2009).

for practical applications, the speed of the subclass of transition-based dependency parsers has been very appealing.


Table 3 gives statistics of the three datasets.

6 over 99% of the trees are projective in all datasets.


Also, despite the fact that the graph-based MST-Parser achieves a similar result to ours on PTB (CoNLL dependencies), our parser is nearly 100 times faster.

our transition-based parser has a great advantage in LAS, especially for the fine-grained label set of Stanford dependencies.


We hypothesize that this sentence-based approach, which exploits a training set in which each sentence in a post is labeled with its reason, would achieve better performance than a multilabel text classification approach to post-level RC, which learns to determine the subset of reasons a post contains directly from a training set in which each post is labeled with the corresponding set of reasons.

we hypothesize that we could achieve better results for post-level RC by learning from sentence-level than from post-level reason annotations, as sentence-level reason annotations can enable a learning algorithm to accurately attribute an annotated reason to a particular portion of a post.


After the annotators completed the aforementioned steps, they were asked to collapse all the reason classes that occur in less than 2% of the sentences annotated with non-NONE classes into the OTHER class.

all the sentences that were originally annotated with one of these infrequent reason classes will now be labeled as OTHER.


In P2, on the other hand, we recast SC as a sequence labeling task.

we train a SC model that assumes as input a post sequence and outputs a stance sequence, with one stance label for each post in the input post sequence.


To avoid this problem, we employ joint inference.

we first train a SC system and a RC system independently of each other.


The post-reason constraints ensure consistency between the predictions made by the SC and the RC systems.

(1) if there is at least one reason supporting the for stance, the post must be assigned a for label; and (2) a for post must have at least one for reason.


This motivates the design of J3, which we build on top of J2.

to incorporate the reason labels predicted for the preceding post in a post sequence, we augment the feature set of the stancespecific reason classifiers with a set of reason features, with one binary feature for each reason.


We find that in terms of SC accuracy, J3 is significantly better than P2 on ABO and GAY, and yields slightly, though insignificantly, better performance on the remaining two domains.

our results suggest that joint modeling of SC and RC has a positive impact on SC performance on all domains, and the impact can sometimes be large enough to yield significantly better results.


In the binary classification task for predicting reason i, we train a binary classifier c i using the one-versus-all training scheme.

to train c i , we create one training instance for each post p in the training set, labeling it as positive if and only if p contains reason i.


Hence, adjusting the threshold amounts to adjusting the number of test instances classified as positive, thus addressing the bias problem mentioned above.

we adjust the thresholds of the classifiers as follows.


Note that since P1 and P2 are pipeline systems, the binary classifiers they use to predict a test post's reasons depend on the post's predicted stance.

if a test post is predicted to have a positive (negative) stance, then only the reason classifiers associated with the positive (negative) stance will be used to predict the reasons it contains.


For the four domains, 75-83% of the errors can be attributed to the system's inability to decide whether a sentence describes a reason or not.

in 51-54% of the erroneous cases, a reason sentence is misclassified as NONE.


Owing to the presence of hidden data, we estimate the model parameters using the EM algorithm.

we use EM to iteratively estimate the model parameters from data in which each overt pronoun is labeled with the probability it corefers with each of its candidate antecedents and apply the resulting model to re-label each overt pronoun with the probability it corefers with each of its candidate antecedents.


So we can rewrite P (p|k, c, l=1) as follows: Moreover, we assume that (1) given p and c's context, the probability of c being the antecedent of p is not affected by the context of the other candidate antecedents; and (2) k c is sufficient for determining whether c is the antecedent of p. So, Furthermore, we assume that given context k, each candidate antecedent of p is generated with equal probability.

given Equations (2), (3), (4) and (5), we can rewrite P (l=1|p, k, c) as: As we can see from Equation (6), our model has two groups of parameters, namely P (p a |c a , l=1) and P (l=1|k c ).


This condition is motivated by our observation that 99.56% of the ZPs in our evaluation corpus (i.e., OntoNotes 5.0) are surface or deep subjects.

we impose this condition so that we can focus our efforts on learning a model for resolving overt pronouns that are subjects.


Given P (l=1|p, k, c), the goal of the M-step is to (re)estimate the model parameters, P (p a |c a , l=1) and P (l=1|k c ), using maximum likelihood estimation.

p (p a |c a , l=1) is estimated as follows: where Count(c a , l=1) is the expected number of times c has attribute value c a when it is the antecedent of p; |a| is the number of possible values of attribute a; θ is the Laplace smoothing parameter, which we set to 1; and Count(p a , c a , l=1) is the expected number of times p has attribute value p a when its antecedent c has attribute value c a .


Given attribute values p ′ a and c ′ a , we compute Count(p ′ a , c ′ a , l=1) as follows: is estimated as follows: where Count(k c ) is the number of times k c appears in the training data, and Count(k c , l=1) is the expected number of times k c is the context surrounding a pronoun and its antecedent c. Given context k ′ c , we compute Count(k ′ c , l=1) as follows: To start the induction process, we initialize all parameters with uniform values.

p (p a |c a , l=1) is set to 1 |a| , and p (l=1|k c ) is set to 0.5.


Given an AZP z, we determine its antecedent as follows: where P R is our set of 10 Chinese overt pronouns and C is the set of candidate antecedents of z.

we apply Formula (11) to each AZP z, searching for the candidate antecedent c and overt pronoun p that maximize P (l=1|p, k, c) when p is used to fill the ZP gap left behind by z.


The only difference is that the set of candidate antecedents of z may now include those NPs that are used to fill the gaps of the AZPs resolved so far.

this incremental resolution procedure may increase the number of candidate antecedents of each AZP z.


We determine the Animacy of a candidate antecedent c heuristically.

we first check the NP type of c. If c is a pronoun, we look up its Animacy in Table 1.


If c is a pronoun, we look up its Gender in  its Gender based on its Animacy.

if c is inanimate, we set its Gender to neuter.


's (2005) methods for computing selectional preferences.

for each verb and each noun that serves as a subject in Chinese Gigaword, we compute their mutual information (MI).


On the other hand, since our supervised resolvers are meant to be reimplementations of existing resolvers, we follow previous work and let them employ a supervised AZP identification system.

we employ the one described in Chen and Ng (2013).


It is worth mentioning that while the two resolvers achieved the same overall performance, their outputs differ a lot from each other.

the two models only agree on the antecedents of 55% of the AZPs in Setting 1.


To investigate the contribution of context and each attribute to overall performance, we conduct ablation experiments.

in each ablation experiment, we remove exactly one probability term from the model and retrain it.


To focus on errors attributable to AZP resolution, we analyze our full model under Setting 1.

we randomly select 100 AZPs that our model incorrectly resolves under Setting 1.


The clusters are scored according to the signature term method of Lin and Hovy (2000), which assigns an importance score to each term accord-   ing to how much more often it appears in the source text compared to some irrelevant background text using a log likelihood ratio.

the score of a cluster is equal to the sum of the importance scores of the set of lemmata in the cluster.


We also measure the quality of sentences at a syntactic or shallow semantic level that operates at the level of dependency triples by a measure that we call Pyramid BE.

we extract all of the dependency triples of the form t = (h, r, a) from the sentence under evaluation and the gold-standard summaries, where h and a are the lemmata of the head and the argument, and r is the syntactic relation, normalized for grammatical voice and excluding the collapsed edges which are mostly noun-phrase-internal elements and grammatical particles.


Despite its apparent difficulty, there is reason to be hopeful for text-to-text generation techniques even in such a scenario.

we showed in earlier work that almost all of the caseframes, or pairs of governors and relations, in humanwritten summaries can be found in the source text or in a small set of additional related articles that belong to the same domain as the source text (e.g., natural disasters) (Cheung and Penn, 2013).


Here, we examine how well distributional similarity determines the appropriateness of a source-external PR pair in a summary.

we measure its similarity to the nearest PR pair in the source text.


This illustrates the challenge of mining sourceexternal text for abstractive summarization, and demonstrates the need for a more structured or detailed semantic representation in order to determine the PR pairs that would be appropriate.

the kind of simple event coreference method based solely on distributional semantics that we used in Section 3.3.1 is unlikely to be sufficient when moving beyond the source text.


The categories used in our experiments were automatically mined from the web, by following existing work (Hearst, 1992, Pantel andRavichandran 2004;Snow et al., 2005;Talukdar et al., 2008;Zhang et al., 2011).

we applied Hearst patterns (e.g., "NP [,] (such as | including) {N P, } * {and|or} NP") and isa patterns ("NP (is|are|was|were|being) (a|an|the) NP") to a large corpus containing 3 billion English web pages.


Note that in this comparison, to be fair, we changed our algorithm to avoid using WordNet in identifying taxonomic relations.

in the SIWN algorithm, all operations of "≈ W N " are replaced with normal string-matching comparison, and all "≫ W N " relations are falsified.


First, we propose a novel model for biomedical event extraction based on MLNs that addresses the aforementioned limitations by leveraging the power of Support Vector Machines (SVMs) (Vapnik, 1995;Joachims, 1999) to handle high-dimensional features.

we (1) learn SVM models using rich linguistic features for trigger and argument detection and type labeling; (2) design an MLN composed of soft formulas (each of which encodes a soft constraint whose associated weight indicates how important it is to satisfy the constraint) and hard formulas (constraints that always need to be satisfied, thus having a weight of ∞) to capture the relational dependencies between triggers and arguments; and (3) encode the SVM output as prior knowledge in the MLN in the form of soft formulas, whose weights are computed using the confidence values generated by the SVMs.


Intuitively, higher (smaller) the confidence, higher (smaller) the weight.

the weights of the soft unit clauses are set as follows.


This greatly reduces the memory requirement of the inference step.

for every grounding of sid, we get a set of nodes in the Markov network that are disconnected from the rest of the Markov network and therefore independent of the rest of the network.


This result ensures that to approximate the expected counts E w (n j ), it is sufficient to keep exactly one sentence's groundings in memory.

e w (n j ) can be written as , where e w (n k j ) indicates the expected number of satisfied groundings of the j th formula in the k th sentence.


To generate features, we employ the supporting resources provided by the organizers.

sentence split and tokenization are done using the GENIA tools, while part-of-speech information is provided by the BLLIP parser that uses the self-trained biomedical model (McClosky, 2010).


After inference, a postprocessing step is required to generate biomedical events from the extracted triggers and arguments.

for binding events, we employ a learning-based method similar to Björne and Salakoski (2011), while for the other events, we employ a rule-based approach similar to Björne et al.


On the two hardest, most complex tasks, detecting regulation events (which have recursive structures and more joint dependencies than other event types) and detecting binding events (which may have multiple arguments), our system performs better than both Miwa12 and Riedel11.

4 our system's F1 score for regulation events is 46.84, while those of Miwa12 and Riedel11 are 45.46 and 44.94 respectively.


Our system has a marginally lower score (by 0.11 points) than Miwa12, which is the best performing system on this dataset.

our system achieves a higher recall but a lower precision than Miwa12.


An examination of the final segmentation suggests that the weight grammar has learned that initial syllables tend to be heavy.

across eight runs, 98.1% of RhymeI symbols rewrote to HeavyRhyme, whereas only 54.5% of Rhyme symbols (i.e.


The most commonly used tag set is {Single, Begin, Middle, End}.

s means the character forms a single word, B/E means the character is the begining/ending character of the word, and M means the character is in the middle of the word.


For development set, we randomly select a small subset (about 10%) of the training data.

2000 sentences are selected for PKU corpus and 8000 sentences for MSRA corpus.


Since our joint model does not involve any additional parameters, we reuse the parameters of the HDP-based model and HMM-based model in the joint model.

we set α 1 = 1000.0, α = 10.0, p s = 0.5 for the HDP-based model and set θ = 1.0, σ = 0.01 for the HMM-based model.


From Table 2, we can see that the joint model (Joint) outperforms all the presented systems in F-Score on all testing corpora.

comparing "HDP+HMM" with "HDP", the former model increases the overall F-Score from 68.7% to 75.3% (+6.6%) in PKU corpora and from 69.9% to 76.3% (+6.4%) in MSRA corpora, which proves that the character information in the HMM-based model can actually enhance the performance of the HDP-based model.


But our proposed joint model solves the most combinational and overlapping ambiguities, on both PKU and MSRA corpora.

compared to nVBE, the joint model correctly solves 171/871 more combinational ambiguities on PKU/MSRA corpora, which is a 0.6%/13.8% relative error reduction.


Phones and Syllables In addition to letterbased segmentation, we also consider other subword units that stem from word acoustics.

we consider segmentation using phones and syllables, which are available for the Babel data we work with.


We can see that when we select more data, both coverage and accuracy fall.

using the sentence alignment score, we can rank sentences with high coverage and accuracy first.


Thus, we use the minimum divergence prior for the token level features, and a standard zero-mean prior for the sequence features.

the objective function of the CRF correction model becomes: where F 1 is the set of features referring to only one label as in the DPM maxent model and F 2 is the set of features over label pairs.


We resolve this by first learning the DPM model on the projected labels, after which we automatically label our correction training set with predicted tags from the DPM model.

we augment the annotated training data from (t M , w M ) to become (t M , t U , w M ).


A lot of work has been done on the AL field in the past years (see (Settles, 2012) for an exhaustive overview).

aL proved its usefulness in sequence labeling tasks (Settles and Craven, 2008).


However, in several implementations the parser is associated with a discriminative model that, on the basis of some features of the current configuration, always chooses a single transition.

the model is used to run the parser as a pseudo-deterministic device.


Recall that transitions are partial functions, meaning that they might be undefined for some configurations.

the shift transition is only defined for configurations with a non-empty buffer.


(2014) for their arc-standard dynamic oracle.

we use the same training algorithm, features, and root node position.


However, there is a data sparseness problem because of the limited size of the current Treebank.

treebank cannot provide enough training data to train a classifier properly.


The accuracy of the Vt-N classifier declines by 0.7% without semantic type information for unknown words.

lexical semantic information improves the accuracy of the Vt-N classifier.


(2010) highlights the benefit of word embeddings on sequential labeling tasks.

in our experiments, we used two recently proposed algorithms, word2vec  and glove (Pennington et al., 2014), which are simple and scalable, although our method could use other word embeddings.


The best combination for the development set is chosen after training that uses random 20% of the training set at the second stage, and Algorithm 1 is terminated when the all token accuracy of the development data has been declining for 5 epochs at the first stage.

32 × 8 random combinations of α, β, and θ for f nn were tested.


In this section, we discuss the strengths and weaknesses of the pipeline and SMT systems, and show how system output combination improves performance.

we compare P1, S1, and P1+S1, although the discussion also applies to P2, S2, and P2+S2.


We further evaluate our system combination approach by making use of the corrected system outputs of 12 participating teams in the CoNLL-2014 shared task, which are publicly available on the shared task website.

7 we combined the system outputs of the top 2, 3, .


We adopt a max-margin framework for this learning problem.

we aim to find parameter values that score the gold target trees higher than others: where ξ i ≥ 0 is the slack variable (non-zero values are penalized against) and ŷ i − y 1 is the hamming distance between the gold treeŷ i and a candidate parse y.


To avoid that, we conduct a merge process to integrate nodes with exactly the same names and contexts into one, combing linked nodes and intent features together.

for a set of nearly duplicate nodes ω the calculation of new node's features can be written as: we gather candidate nodes retrieved by LSH and then calculate ISim for them with η setting to 0.


The use of targeted patterns in relation extraction has been suggested before (Hearst, 1992;Mesquita et al., 2013).

in (Tokunaga et al., 2005), the patterns "A of D" or "what is the A of D" extract noun-phrase A attributes from queries and documents, for phrase descriptors D of the class.


Nevertheless, facts derived from queries likely serve as a complement, rather than replacement, of facts from documents.

facts extracted from queries make no attempt to iso-late the value of the respective properties, whereas facts extracted from documents usually do.


First, the average precision of run R DS is higher than for run R D .

when extracting from document sentences in R DS and R D , the patterns proposed in our method give fewer and more accurate facts than the patterns from .


In general, the phenomenon affects facts extracted from text by previous methods (Van Durme and Paşca, 2008;.

it affects facts extracted from both documents or queries in our experiments.


For mapping phrases to relations, we employ the resources from PATTY (Nakashole et al., 2012) and ReVerb (Fader et al., 2011).

we first compute the associations between the ontological relations in DBpedia and the relation patterns in PATTY and ReVerb through instance alignments as in (Berant et al., 2013).


In the MLN, we design several predicates to resolve the ambiguities in phrase detection, mapping phrases to semantic items and semantic item grouping.

we design a hidden predicate hasPhrase(i) to indicate that the i-th candidate phrase has been chosen.


The concrete patterns can be generated through these meta patterns with training data.

we use two types of formulas for the joint decisions: Boolean and Weighted formulas.


2 ) is 1 if e 2 belongs to t 2 and zero otherwise.

candidate e 2 s must be proposed to be instances of the proposed t 2 -this is a hard constraint, but can be softened if desired, like Ψ E 1 ,R,E 2 .


(2013) have done the pioneer work on QDE, by leveraging question-user comparisons extracted from the question answering threads.

they assumed that the difficulty level of a question is higher than the expertise level of the asker (i.e.


The result is r = 0.6424, implying that the difficulty gap is positively correlated with the textual distance.

the more similar two questions' textual descriptions are, the more close their difficulty levels are.


The key idea is to propagate difficulty scores from well-resolved questions to cold-start ones according to their textual similarities.

suppose that there exists a set of well-resolved questions whose difficulty scores have already been estimated by a QDE method.


The first two sets were used to evaluate the methods in estimating difficulty scores of resolved questions.

the development set was used for parameter tuning and the test set was used for evaluation.


Note that previous work on debate stance recognition has also employed constraints to improve the inference process.

in stance prediction, it is typical to employ so-called author constraints (e.g., Thomas et al.


In order to determine how the source node votes over a QuestionPair edge, we need to calculate some probabilities.

we need to determine the probability that a user will vote for possible answer k in the target question Q I given that she voted for answer l in the source question Q J : where C(Q In , Q J l ) is the number of users who voted for answer n in Q I and answer l in Q J , and A(Q I ) is the set of possible answers on Q I .


ProPPR allows a fast approximate proof procedure, in which only a small subset of the full proof graph is generated.

if α upper-bounds the reset probability, and d upperbounds the degree of nodes in the graph, then one can efficiently find a subgraph with O( 1 α ) nodes which approximates the weight for every node within an error of d (Wang et al., 2013), using a variant of the PageRank-Nibble algorithm of Andersen et al (2008).


The overview of our framework is shown in Figure 2.

we define the following four main components: 1) Microblog emotion classification: We train emotion classification model by automatically acquiring training data using the emoticons.


We combine the features which have been proven effective by previous work, such as punctuation and emotion lexicons.

we use the following features : 1) Words appearing in the microblog message serve as word features, 2) Number of "!"


For each community, we compare the event list E c and the generated burst list B c to compute the above metric.

the precision, recall and F-measure for a community c are defined as follows: where E c j is the occurring time of the j-th event in community c, B c k is the k-th identified burst for community c.


The importance of other rhetorical relation features can also be explained by examining conversations in which they appear.

eXPLANATION relations often link authors who share viewpoints in a debate, especially when one author is trying to support the claim of another.


A major source of error in detecting disagreement arises because of inaccuracies in our discourse parser.

document-level discourse parsing is a challenging task, with relatively few parsers available at the time of this analysis Hernault et al., 2010).


Examining the relation labels confusion matrix for the discourse parser in Figure 2, some of the chosen rhetorical features make even more sense.

the confusion of ELABORATION and EXPLANATION may account for the perceived importance of ELABORATION relations in the analysis.


are in both the +effect word set and the -effect word set.

these words have both +effect and -effect meanings.


The vertex degree matrix LGC defines the cost function Q which integrates two penalty components, global smoothness and local fitting (µ is the regularization parameter): The first part of the cost function is the smoothness constraint: a good classifying function should not change too much between nearby points.

if x i and x j are connected with an edge, the difference between them should be small.


To use more combined knowledge, the gloss classifier and BiGraph* can be combined.

for WordNet gloss information, the gloss classifier is utilized, and for WordNet relations, BiGraph* is used.


We assess the system's accuracy on the newly labeled data by comparing the system's labels with the human's new labels.

accuracy for +effect and -effect is calculated such as: accuracy +ef f ect = # annotated +effect # top 5% +effect data accuracy −ef f ect = # annotated -effect # top 5% -effect data the accuracy means that out of the top 5% of the +effect (-effect) data as scored by the system, what percentage are correct as judged by a human annotator.


It assigns a real-valued score (or weight) to every derivation d ∈ D(x), where Λ ∈ R m assigns a relative importance to different aspects of the derivation independently captured by m feature functions The fully parameterised model can be seen as a discrete weighted set such that feature functions factorise over the steps in a derivation.

h k (d) = e∈d h k (e), where h k is a (local) feature function that assesses steps independently and d = e 1 , e 2 , .


While ψ weights the yield y taking into account all n-grams (including those crossing the boundaries of phrases), ω weights edges in isolation.

ω(y[e i ]) = λ ψ q LM (y[e i ]), where y[e i ] returns the sequence of target words (a target phrase) associated with the edge, and q LM (•) is an upperbound on the true LM probability p LM (•) (see §4.3).


The outgoing edges from the new node are reweighted copies of those leaving the original node.

outgoing edges such as where w = λ ψ q LM (y1y2) /q LM (y2) is a change in LM probability due to an extended context.


We train on the output of a hierarchical reordering model-based system and scale to millions of features learned on hundreds of thousands of sentences ( §4).

we use the expected BLEU objective function (Rosti et al., 2010;Rosti et al., 2011;He and Deng, 2012;Gao and He, 2013;Gao et al., 2014;Green et al., 2014) which allows us to train models that use training data and feature sets that are two to three orders of magnitudes larger than in previous work ( §5).


An extension of the lexicalized reordering model better handles longdistance reordering by conditioning the orientation of the current phrase on a context larger than just the previous phrase (Galley and Manning, 2008).

the hierarchical reordering model does so by building a compact representations of the preceding context using an efficient shiftreduce parser.


Our reordering model is defined as a simple linear model over the basic orientation types, similar to Cherry (2013).

our model defines score s φ (o, e, f ) over orientations o = {M, S, D}, and a sentence pair {e, f, a} with alignment a as a linear combination of weighted indicator features: where φ is a vector of weights, is a set of phrases that decompose the sentence pair {e, f, a}, and u(o, pp i , c i ) is a function that maps orientation o, phrase pair pp i and local context c i to a sparse vector of indicator features.


In our experiments we use n-best lists with unique entries and therefore our definitions do not take into account multiple derivations of the same translation.

our n-best lists are generated by choosing the highest scoring derivationê amongst string identical translations e for f .


10 Previous work on sparse reordering models was restricted to small data sets (Cherry, 2013) due to the limited ability of standard machine translation optimizers to handle more than a few thousand sentences.

recent attempts to scale the margin-infused relaxation algorithm, a variation which was also used by Cherry (2013), to larger data sets showed that more data does not necessarily help to improve test set accuracy for large feature sets (Eidelman et al., 2013).


In the next set of experiments, we shed light on the advantage of training discriminative reordering models with expected BLEU on large training sets.

we start off by estimating a reordering model on only 2,000 sentences, similar to the size of the development set used by Cherry (2013), and incrementally increase the amount of training data to nearly three hundred thousand sentences.


Our next experiment teases those two aspects apart and clearly shows the effect of the objective function.

we compare the traditionally used conditional log-likelihood (CLL) objective to expected BLEU on the French-English translation task in a small feature condition (SparseHRM) Table 4: French-English results on the SparseHRMLocal feature set when when trained with pair-wise ranked optimization (PRO) and expected BLEU (xBLEU).


7 reranker outperforms 1-pass Moses by 3.5 BLEU, indicating a strong performance of the features used on this task.

sOUL is known to be a useful feature for reranking n-best lists on highlyinflected languages such as French.


Figure 4 shows that reranker improves more over 1-pass Moses and that at the same time rewriter improves more over reranker as the sentences are more difficult.

rewriter obtains a 8.6 BLEU improvement over 1-pass Moses on the more difficult quartile, but only a 1.3 BLEU improvement on the least difficult quartile.


This domain captures some of the fundamental difficulties in recent semantic parsing problems.

it requires learning from weaklysupervised data, rather than data annotated with full logical forms, and parsing sentences in a situated environment.


In this domain, the conjunction in the logical form specifies conditions on events.

the event must be a move event and have a specified direction.


At training time, we assume access to a set of N examples , consists of an instruction x (i) , the state s (i) where the instruction is issued and its execution demonstration e (i) .

we know the correct execution for each state and instruction, but we do not know the correct CCG parse and logical form.


Recent advances in natural language processing, with an eye to semantic parsing (Zettlemoyer and Collins, 2005;Liang et al., 2011;Artzi and Zettlemoyer, 2013;Liang and Potts, 2014), use different formalisms and various kinds of learning signals for statistical semantic parsing.


An event e can also refer to a state, where a logical expression is being evaluated over a set of property:value pairs.

we call such an event a condition, and specify the set of possible conditions as follows: e = exp, {l : p : v} k i=0 , temp, exe is a condition to be evaluated, where , temp ∈ {hot, cold} and exe ∈ {executed, monitored} are as specified above.


As our annotated seed is quite small, it is hard to generalize from it to unseen examples.

we are not guaranteed to have observed all possible structures that are theoretically permitted by the assumed grammar.


Here, we see larger improvements over the synthetic-only PCFG.

modeling grammaticality of individual sentences improves the interpretation of the document.


In this work, we propose a novel model for parsing natural language into tree structured semantic representations.

we propose a novel relaxed hybrid tree representation which jointly encodes both natural language sentences and semantics; such representations can be effectively learned with a latent-variable discriminative model where long-distance dependencies can be captured.


Each semantic representation consists of se-mantic units as its tree nodes, where each semantic unit is of the following form: Here m a is used to denote a complete semantic unit, which consists of its semantic type τ a , its function symbol p α , as well as an argument list τ b * (we assume there are at most two arguments for each semantic unit).

each semantic unit can be regarded as a function which takes in other semantics of specific types as arguments, and returns new semantics of a particular type.


One key difference is that we disallow the unary pat-   The features are defined over the (n, m, h) tuples.

in practice, we define features at each level of the relaxed hybrid tree structure h. features are defined over (n, m) tuples where n is a contiguous sequence of natural language words (immediately or remotely) associated with the semantic unit m (recall that h contains both n and m, and each level of h simply consists of a semantic unit and a contiguous sequence of words).


The other term also be computed based on similar equations.

such terms can be computed from even smaller similar terms in a recursive manner.


To overcome this difficulty, we instead find the optimal semantic tree using the following equation: We essentially replace the operation by the max operation inside the arg max.

we first find the best latent relaxed hybrid tree h * that contains the input sentence n, and next we extract the optimal semantic tree m * from h * .


We used the standard evaluation criteria for judging the correctness of the outputs.

our system constructs Prolog queries from the output parses, and uses such queries to retrieve answers from the GeoQuery database.


We thus distinguish our work from several previous works in the literature which focused on semantic parsing under other assumptions.

for example, works such as (Liang et al., 2013;Poon and Domingos, 2009;Clarke et al., 2010) essentially performed semantic parsing under different settings where the goal was to optimize the performance of certain downstream NLP tasks such as answering questions, and different semantic formalisms and languagespecific features were usually involved.


5 The span features, which are able to capture certain long-distance dependencies, also play important roles.

if such features are excluded, our model's performance on three languages (Greek, English, Thai) will drop.


The method greedily chooses the farthest point from the current subspace until the given number of anchors have been found.

at the early stages of the algorithm, the words associated with the farthest points are likely to be infrequent and idiosyncratic, and thus form poor bases for human interpretation and topic recovery.


We find that CCE improves over both standard CE as well as strong baselines from the shared task.

our final average accuracies are better than all entries in the shared task that use the same number of tags.


So, we used treebanks for 11 languages from the CoNLL 2006/2007 shared tasks (Buchholz and Marsi, 2006;Nivre et al., 2007) other than those used in our POS induction experiments.

we used Arabic, Bulgarian, Catalan, Czech, English, Spanish, German, Hungarian, Italian, Japanese, and Turkish.


We use their strategy, though we experiment with two others as well.

3 we estimate held-out data loglikelihood via the method of Bengio et al.


However, by comparing NAÏVE to ALIGN, we see a consistent benefit from aligning tags before voting, leading to our highest average accuracies.

using MATCHLM+UNIV and ALIGN, we improve over CE by 2.5 in M-1 and 4.5 in 1-1, also improving over the best results from the shared task.


While there are existing automated metrics for assessing translation quality (Papineni et al., 2002;Banerjee and Lavie, 2005;Snover et al., 2006), these must be modified to find the necessary compromise between translation quality and expeditiousness.

a good metric for simultaneous translation must achieve a balance between translating chunks early and translating accurately.


The probabilistic equivalent of this is the class of Strongly Congruential PCFGs (SC-PCFGs), defined as all the PCFGs G which, for any non .


In a first experiment (vaguely similar to the one done by Luque and López (2010)), we constructed the best possible SC-CFG consistent with the merges done in the first phase and assigned probabilities to this grammar using Inside-Outside.

we ran the second phase of our system in a supervised fashion by using the treebank to decide which are the best congruence classes to choose as non-terminals.


In §3, we show that the existing heuristic word sampling is merely a special case of a non-interactive active learning method (Gu and Han, 2012).

the existing sampling is identical to the case where a special graph called a "multi-complete graph" is fed to a non-interactive active learning method.


The cluster assumption is an assumption that heavily connected nodes in a graph should have similar labels.

the cluster assumption states that weights of edges and labels of nodes should be consistent.


In this subsection, we qualitatively evaluate our results to determine the types of nodes that are sampled when domain specificity is introduced.

we evaluate what words are selected as samples in the "BNC + domain" graph.


The usefulness of the device-dependent readability is proven by applying it to news article recommendation.

different importance weights for readability factors are considered according to device type when recommending news articles.


People usually bring continuous sentences into their mind at the same time, and interpret them as a single unit (Okazaki et al., 2005).

a reader prefers text whose sentences are smoothly connected to text whose sentences are independent of one another.


Therefore, readability is calculated as a weighted linear sum of all readability factors.

r(t|d) is computed by where M is the number of readability factors.


The order between ranks a 1 a 2  ... a m should be satisfied by the criterion of the recommendation system.

assuming that the system has a score function score(a i ), score(a i ) > score(a j ) has to be met if a i a j .


After that, the selected articles are re-ranked by another criterion, the device-dependent readability.

the final rank of an article within the selected set is determined by another function, rerank.


The Galaxy Note 1 with a 5-inch screen is used as the smart phone, Galaxy Tab 10.1 with a 10.1inch screen is used as the tablet, and A4-size paper is used for the paper.

the human annotators read and rated 74 articles per device.


These results imply that readers are negatively sensitive to the length of an article because of the small display size of a smart phone.

in the smart phone, longer articles are recognized as difficult to read compared to shorter ones.


Each graph in these figures compares the performance of various devices when the readability for a specific device is used.

figure 2 depicts the NDCG@k * scores for the recommended news articles when the articles are shown in the smart phone, the tablet, and the paper respectively.


In English, the abbreviations (similar to acronyms) are usually formed by concatenating initial letters or parts of a series of words.

english abbreviation generation is based on words in the full form.


In order to test the performance of our proposed approach, we built our own datasets since citationenhanced evaluation benchmarks are not available for keyphrase extraction tasks.

we compiled two datasets consisting of research papers from two top-tier machine learning conferences: World Wide Web (WWW) and Knowledge Discovery and Data Mining (KDD).


We tuned our classifier trained on WWW and KDD to return as keyphrases only those that had an extremely high probability to be keyphrases.


After obtaining the relative risk levels of the companies, the ranking task can be defined as follows: Given a collection of financial reports D, we aim to rank the companies via a ranking model f : R p → R such that the rank order of the set of companies is specified by the real value that the model f takes.

f (d i ) > f (d j ) means that the model asserts that c i c j , where c i c j means that c i is ranked higher than c j ; that is, the company c i is more risky than c j .


Thus, we believe that, it is possible to distinguish the plagiarized fragments from the original ones on the basis of the frequency of their character n-grams in the suspicious document.

if many of the character n-grams of a fragment are infrequent in the document, it would be probably a plagiarized fragment.


Statistical language modeling (LM) that purports to quantify the acceptability of a given piece of text has long been an interesting yet challenging research area.

language modeling for information retrieval (IR) has enjoyed remarkable empirical success; one emerging stream of the LM approach for IR is to employ the pseudo-relevance feedback process to enhance the representation of an input query so as to improve retrieval effectiveness.


While an individual may have an inherent propensity to shift topics or not, we argue that the topic shifting tendency he or she displays can vary based on the social settings in which he or she interacts and his or her status within those settings.

the same discourse participant may behave differently in different social situations and at different points in time.


We obtain a highly significant (p = 0.002) negative correlation between topic shift tendency of a candidate (PI) and his/her power.

the variation in the topic shifting tendencies is significantly correlated with the candidates' recent poll standings.


What we would really like to say is that the best rank one approximation B (1) (under some norm) of B is ∝ P (w i ) P (w i−1 ).

while this statement is not true under the 2 norm, it is true under generalized KL divergence (Lee and Seung, 2001): generalized KL divergence preserves row and column sums: if M (κ) is the best rank κ approximation of M under gKL then the row sums and column sums of M (κ) and M are equal (Ho and Van Dooren, 2008).


Now consider B •0.5 : Note how the row sum vector has been altered.

since w 1 (corresponding to the first row) has a more diverse history than w 2 , it has a higher row sum (compared to in u where w 2 has the higher row sum).


Moreover, it provides a natural way of setting d * based on the Good-Turing (GT) estimates employed by KN smoothing.

we can set d * to be the (η + 1) th root of the KN discount D that can be estimated via the GT estimates.


answer reading comprehension questions that test understanding of the underlying structure.

we consider processes in biology textbooks such as this excerpt and the question that follows: ".


Formulation Given the two sets of variables, the objective of inference is to find a global assignment that maximizes the score.

the objective can be stated as follows: Here, y and z refer to all the argument and relation variables respectively.


The query can prove either the correctness or incorrectness of the answer being considered.

either we get a valid match for an answer (proving that the corresponding answer is correct), or we get a refutation in the form of a contradicted causal chain (thus proving that the other answer is correct).


In recent years, several lines of work addressed semantic parsing using various formalisms and levels of supervision (Zettlemoyer and Collins, 2005;Wong and Mooney, 2006;Clarke et al., 2010;.

krishnamurthy and kollar (2013) learned to map natural language utterances to referents in an image by constructing a kB from the image and then mapping the utterance to a query over the kB.


In order to make any concrete statements about the number of nonzero elements in X, it is necessary to make some assumptions about the distribution of word co-occurrences.

we will assume that the number of co-occurrences of word i with word j, X i j , can be modeled as a power-law function of the frequency rank of that word pair, r i j : The total number of words in the corpus is proportional to the sum over all elements of the cooccurrence matrix X, where we have rewritten the last sum in terms of the generalized harmonic number H n, m .


These results suggest that the power gained from the the more sophisticated MIML-RE model is best used in conjunction with a small amount of training data.

using MIML-RE as a principled model for combining a large distantly labeled corpus and a small number of careful annotations yields significant improvement over using either of the two data sources alone.


For each relation, let L k and R k be the set of entities with a compatible type to the k-th relation.

(e i , r k , e j ) is a feasible triple if and only if e i ∈ L k and e j ∈ R k .


The rows and columns of X k lr map to the entities in A k l and A kr , respectively.

entries of X k but not in X k lr do not satisfy the type constraint and are ignored from the computation.


Based on this information, the compatible entity type constraint of each relation can be easily identified.

we examined the entities and relations that occur in the triples of the training data, and counted all the types appearing in these instances of a given relation legitimate.


Comparing TRESCAL with RESCAL, each iteration of TRESCAL takes 1,608 seconds, while that of RESCAL takes 7,415 seconds.

by inducing the entity type information and constraints, TRESCAL enjoys around 4.6 times speed-up, compared to an improved regularized version of RESCAL.


This method is based on a two-layer graphical model, the first layer corresponding to  a relation classifier at the mention level, while the second layer is aggregating the different prediction for a given entity pair.

this second layer capture dependencies between relation labels, such as the fact that two labels cannot be generated jointly (e.g.


Upon examination of the data, this can partly be explained by the fact that almost no sentences extracted for the relation /people/person/place of birth in fact express this relation.

many facts present in Freebase are not expressed in the corpus, and are thus impossible to extract.


Large scale experiments on Freebase and a Wikipedia/NY Times corpus show that jointly embedding brings promising improvement in the accuracy of predicting facts, compared to separately embedding knowledge graphs and text.

jointly embedding enables the prediction of facts containing entities out of the knowledge graph, which cannot be handled by previous embedding methods.


We conduct extensive large scale experiments on Freebase and Wikipedia corpus, which show jointly embedding brings promising improvements to the accuracy of predicting facts, compared to separately embedding the knowledge graph and the text corpus, respectively.

jointly embedding enables the prediction of a candidate fact with out-of-kb entities, which can not be handled by any existing embedding methods.


If two words w and v concur in some context, e.g., a window of text, then there is a relation r wv between the two words.

we can state the triplet of (w, r wv , v) is a fact.


There are around 45 million such anchors in our text corpus and 41,970,548 of them refer to entities in E. Another mechanism utilizes the name graph constructed through names of entities.

for each training triplet (h, r, t), suppose h and t have entity names w h and w t , respectively and w h , w t ∈ V, the training triplet contributes (w h , r, w t ), (w h , r, t), and (h, r, w t ) to the name graph.


Therefore, choosing between user study 1 and 2 is not a straightforward decision.

designing the two user studies in this way helps us to answer the question: "Does the fact that raters can read all the reviews affect their ratings?".


If p a i (sentiment distribution for aspect a i ) is not in the confidence interval of p f j , we then are confident that they follow different distributions.

if aspect phrase f j ∈ aspect a i , we are confident that p a i is in the confidence interval of p f j .


In Section 5.1 the accuracy of the PM algorithm is tested on various corpora containing annotated misspellings of English words.

we were interested to see the results when the edit distance between the misspelled pair is bigger than 3, because handling bigger values for k is crucial for finding misspelling errors produced by non-native speakers.


Birkbeck is a heterogeneous collection of documents, so in the experiments below we refer to each document separately.

we distinguish between misspellings of native speakers vs. misspelling of non-native speakers.


Though more sophisticated than methods based on rough error counts, the conjoint model is bound to several constraints that limit its usability.

the application of conjoint analysis in this context requires to: i) operate with semi-automatically created (hence artificial) data instead of real MT output, ii) manually define different levels of severity for each error type (e.g.


In general we notice a lower agreement on the intermediate quality scores, while annotators tend to agree on very bad and, even more, on good translations.

we see that the agreement for ENAR is systematically lower than the values measured for the other languages on all the score classes.


We compute all automatic scores by relying on a single reference and by means of standard packages.

automatic scores on Chinese are computed at the character level.


We assess the quality of our mixed linear models (MLM) by comparing their prediction capability with a sequence of simpler linear models including only fixed effects.

we built five univariate models and two multivariate models.


Prediction performance is computed in terms of Mean Absolute Error (MAE), 4 which we estimate by averaging over 1,000 random splits of the data in 90% training and 10% test.

for the human quality classes we pick the integer between 1-5 that is closest to the predicted value.


Remarkably, for some translation directions, some of the metrics show a sensitivity to errors that is very similar to that of human judges.

bLEU for ENZH and ENAR, and GTM for ENZH show a very high correlation with the human sensitivity to translation errors, with Pearson correlation coefficient ≥ 0.97.


8 As shown in Table 3, the BNN models outperform the baseline by a large margin in all tasks and languages.

word prediction accuracy at top-1 increases by +6.4%, +24.6% and +9.0% absolute in English-Russian, English-Czech and English-Bulgarian respectively, without the use of any features based on linguistic annotation.


2011, a bilingual LM (BiLM), which defines elementary translation events t 1 , ..., t n as follows: where e i is the i-th target word and A : E → P(F ) is an alignment function, E and F referring to target and source sentences, and P(•) is the powerset function.

the i-th translation event consists of the i-th target word and all source words aligned to it.


Our current approach is still strictly tied to the number of target tokens.

we are interested in exploring ways to better capture the notion of syntactic cohesion in translation (Fox, 2002;Cherry, 2008) within our framework.


In addition to exchangeability, another important property of type-based MCMC is that the type of each site to be sampled should be independent of the assignment of the other sites sampled at the same time.

in our case, setting the cut value of each site should not change the (r 1 , r 2 , r 3 ) triple of another site.


Dropout prevents co-adaptation of hidden units by randomly dropping out-i.e., setting to zero-a proportion p of the hidden units during fowardbackpropagation.

given the penultimate layer z = [ĉ 1 , .


Thus, the bigram distribution of one document is generated according to both the bigram distribution of the domain and the bigram distributions of other documents in the same domain.

h represents the bigram distributions of the documents in the m th domain except the j th document.


Our basic method relies on analysis of lexical overlap between the component words and the definitions of the MWE in Wiktionary, in the manner of Lesk (1986).

if a given component can be found in the definition, then it is inferred that the MWE carries the meaning of that component.


By extending the GP regression framework to vector-valued outputs we obtain the so-called coregionalisation models.

we employ a separable vector-valued kernel known as Intrinsic Coregionalisation Model (ICM) (Álvarez et al., 2012).


There is extensive empirical evidence that saccade velocity scales with saccade amplitude.

the relationship between logarithmic peak saccade velocity and logarithmic saccade amplitude is linear over a wide range of amplitudes and velocities; this is known as the main sequence relationship (Bahill et al., 1975).


A gradient step is then made to optimize the pairwise hinge loss: We use m = 0.1 in our experiments.

this loss function is referred to as the WARP loss in  and is used to approximately optimizing the top of the ranked list, useful for metrics like precision and recall@k. the search for a negative candidate tag means that more energy is spent on improving the ranking performance of positive labels already near the top of the ranked list, compared to only randomly sampling of negatives, which would optimize the average rank instead.


The words in a text are averaged to produce an embedding of the text, and hashtags are ranked by similarity to the text embedding.

the model is of the form: where the post w is represented as a bag of words (a sparse vector in R N ), the tag is a one-hot-vector in R N , and U and V are k × N embedding matrices.


This figure shows that Step 2 achieves higher precision than Step 1 at all levels of recall, indicating that the word sense tagging method in step 2 is useful for Step 1 Step 2 Step  a significant boost of recall, together with a little improvement in precision.

step 2 can extract about 20% relations with relatively high precision (about 90%).


We can additionally reject relation annotations where the types of the arguments do not agree with the expected types of the relation.

we only accept a relation annotation s→R(m 1 , m 2 ) when EntityTypes(e 1 ) ∩ τ 1 = ∅ and EntityTypes(e 2 )∩τ 2 = ∅, where m 1 is linked to e 1 , m 2 is linked to e 2 , EntityTypes provides the set of valid types for an entity, τ 1 is the set of allowed types for the first argument of target relation r, and τ 2 for the second argument.


Information in visually rich formats such as PDF and HTML is often conveyed by a combination of textual and visual features.

genres such as marketing flyers and info-graphics often augment textual information by its color, size, positioning, etc.


lack a centralized searchable database with industry offerings.

the commercial real estate industry (unlike residential real estate) does not have a centralized database or an established source of information.


In this study we explored a supervised machine learning approach to the task of identifying listing information from commercial real estate flyers.

we focused on the recognition of 12 types of named entities as described in Table  1 below.


Named Entities referring to listing address information showed the most significant improvements.

the F1-score for mentions of Neighborhoods (typically prominently shown on the first page of the flyers) improved by 19%; F1score for mentions of the listing State improved by 9%; and Street, City, Zip by roughly 4% each, all produced poor results on our dataset and failed to accurately compute the pixel location of text elements.


The Contextual Temporal Profile (CTP) of an entity e at time t, C e (t), consists of the context within which e is mentioned.

c e (t) consists of uni-grams and bi-grams generated from the 5-grams(Google Books Ngram) or sentences (Gigaword) that mention e at time t. Notice that the cTPs can contain context units (bi-grams or uni-grams) that are simply noise.


(2010) tightens the assumption of distant supervision in the following manner: "Given a pair of entities and their mentions in sentences from a corpus, at least one of the mentions express the relation given in the database".

it models the problem as that of multi-instance (mentions) single-label (relation) learning.


Input: Source sentence x, a rule set for x, and w. Output: {r} is a derivation consisting of one rule r. 7: for k in (i + 1 .. i + p) do p is the phrase length limit 9: 11: on some translation metric (such as BLEU (Papineni et al., 2002)).

for a training sentence pair x, y , if a pair of its translations y 1 = e(d 1 ) and y to hold after tuning.


We use BLEU-4 (Papineni et al., 2002) with "average ref-len" to evaluate the translation performance for all experiments.

the character-based BLEU-4 is employed for EN-CH task.


We thank the three anonymous reviewers for suggestions, and Kai Zhao and Feifei Zhai for discussions.

we thank reviewer #3 and Chin-Yew Lin for pushing us to think about diversity.


We concentrate on modeling the writer's choices related to discourse structure, rather than lexical choice.

our goal is to show that gender, gender environment, and power all affect individuals' choices in complex ways, resulting in patterns in the discourse that reveal the underlying factors.


Since this aspect of language is specifically dependent on a group of people (it is an inherently social function), we assume that the relevant feature is not simply Gender, but Gender Environment.

we make our Hypothesis 2 more precise by saying that a higher number of conventional dialog acts is used in Female Environments.


The latent value y is chosen according to a distribution π.

the ratio between background and topic words is determined by π.


As mentioned above, the current topic trend θ t,k is drawn from the following Dirichlet distribution with the previous trendθ t−1,k where θ t,k,v represents the probability that word v is chosen in topic k at time t. Here our proposed Twitter-TTM adapts the above TTM assumptions to the improved model.

we extend the improved model whereby user interest ϕ t,u and topic trend θ t,k depend on previous states.


However, the TM-LDA does not consider dynamic word distributions.

their model can not capture the dynamics of topic trends.


One challenge of such large scale study, though, remains in the lack of labeled groundtruth data of self-disclosure level.

naturally-occurring Twitter conversations do not come tagged with the level of self-disclosure in each conversation.


This enables us to conduct a fine-grained analysis of how comparison usage interacts with their social context, opening up a research direction with applications in sentiment analysis and opinion mining.

we find that figurative comparisons are more likely to accompany reviews showing extreme sentiment, and that they are uncommon in opinions deemed as being helpful.


We find that similes tend to have indefinite articles in the VEHICLE more often and definite articles less often (Mann-Whitney p < 0.01).

59% of comparisons where the VEHICLE has a indefinite article are figurative, as opposed to 13% of the comparisons where VEHICLE has a definite article.


Instead of the simple bag of words representation of a target document (segment of three paragraphs that contains a target phrase), we investigate the bag of words topic representation for target documents.

we extract topics from paragraphs containing idioms and from paragraphs containing literals using an unsupervised clustering method, Latent Dirichlet Allocation (LDA) (Blei et al., 2003).


Two corresponding term by document matrices will be denoted by M D and MD, respectively, where D denotes a set of documents.

m D represents the original "text" term by document matrix, while mD represents the "topic" term by document matrix.


The middle figure shows a projection onto the topic space that is computed with a restricted vocabulary, while the right figure shows a projection when we place no restriction on the vocabulary.

the vocabulary includes terms from documents that contain both idioms and literals.


My reports went to all British newspapers, with special direct services by me to India, South Africa and West Indies; even to King George V in Buckingham Palace, who loved his cricket.

i was to some extent leading the British public astray.


For a document we calculate the corresponding arousal value a i for each w i , obtaining Let m A be the average arousal value calculated over the entire training data.

the centered arousal value for a training document is obtained by subtracting the training arousal mean is used to center both training and query arousal values.


In this paper, we demonstrate that it is feasible to discover the coherent structure of a text using distributed sentence representations learned in a deep learning framework.

we consider a WINDOW approach for sentences, as shown in Figure 1, where positive examples are windows of sentences selected from original articles generated by humans, and negatives examples are generated by random replacements 2 .


The reranker relies on tree kernels (TKs) to capture the global dependencies between discourse units in a tree.

we design new computational structures of discourse trees, which combined with standard TKs, originate novel discourse TKs.


The latter allows us to represent structured data using the substructure space thus capturing structural dependencies between DT constituents, which is essential for effective discourse parsing.


They can ascertain if portions of a DT are compatible, incompatible or simply not likely to coexist, since each substructure is an exploitable feature.

problematic DTs are expected to be ranked lower by our reranker.


We create the training data for the reranker in a 5-fold cross-validation fashion.

8 we split the training set into 5 equal-sized folds, and train the parsing model on 4 folds and apply to the rest to produce k most probable DTs for each text.


The standard procedure to evaluate discourse parsing performance is to compute Precision, Recall and f -score of the unlabeled and labeled metrics proposed by Marcu (2000b).

10 the unlabeled metric Span measures how accurate the parser is in finding the right structure (i.e., skeleton) of the DT, while the labeled metrics Nuclearity and Relation measure the parser's ability to find the right labels (nuclearity and relation) in addition to the right structure.


Therefore, we measure the oracle accuracy of the k-best discourse parser based on the f -scores of the Relation metric, and our reranking framework aims to optimize the Relation metric.

11 the oracle accuracy for k-best parsing is measured as fol- , where N is the total number of texts (sentences or documents) evaluated, g i is the gold DT annotation for text i, h j i is the j th parse hypothesis generated by the k-best parser for text i, and f -score r (g i , h j i ) is the f -score accuracy of hypothesis h j i on the Relation metric.


And as we can observe, although the proposed framework obtains comparable result compared with existing state-of-state performances regarding all evaluating parameters for discourse parsing.

as for the three measures, no system achieves top performance on all three, though some systems outperform all others for one of the measures.


By doing so, we provide a solid foundation for work on employing knowledge sources for improving recall for coreference resolution (Ponzetto and Strube, 2006;Rahman and Ng, 2011;Ratinov and Roth, 2012;Bansal and Klein, 2012, inter alia).

we make the following contributions: We present a novel framework for coreference resolution error analysis.


We apply the method proposed in this paper to perform a recall error analysis of four state-ofthe-art systems, encompassing deterministic and learning-based approaches.

we identify and characterize a set of challenging errors common to all systems, and discuss strengths and weaknesses of each system regarding specific error types.


We employ for representation a complete onedirectional graph.

we represent an entity e over mentions {m 1 , .


2 Using this representation, we can represent a set of entities as a set of graphs.

given a document we consider the set of reference entities R given by the annotation, and the set of system entities S, given by the system output.


While the total number of errors differs between the systems, the distributions are similar.

around 55% of recall errors made involve only proper names and common nouns.


10 Our framework bears most similarities to the MUC metric, as both are based on the same linkbased entity representation.

when we divide the number of errors extracted from an entity by the size of a spanning tree for that entity, we obtain a score linearly related 11 to the MUC score for that entity (recall for reference entities, precision for system entities).


We characterized the common errors among a broad range of properties.

our analysis highlights and quantifies the usefulness of world knowledge.


In the decoding stage, the best first strategy (Ng and Cardie, 2002) is used to predict the bridging links.

for each a ∈ A ml , we predict the bridging link to be the most confident pair (a, c ante ) among all instances with the positive prediction.


Our rule-based system performs better than a learning-based approach which has access to the same knowledge resources as the rulebased system.

the learning-based system enriched with more features does not yield much improvement over the rule-based system.


In this paper, we propose a Connectivedriven Dependency Tree (CDT) scheme to represent the discourse rhetorical structure in Chinese language, with elementary discourse units as leaf nodes and connectives as non-leaf nodes, largely motivated by the Penn Discourse Treebank and the Rhetorical Structure Theory.

connectives are employed to directly represent the hierarchy of the tree structure and the rhetorical relation of a discourse, while the nuclei of discourse units are globally determined with reference to the dependency theory.


For example, Zhou and Xue (2012) use the PDTB annotation guidelines to annotate Chinese discourse with 98 files from Chinese Treebank (Xue et al., 2005b) of Xinhua newswire.

they adopt a lexically grounded approach and make some adaptation based on the linguistic and statistical characteristics of Chinese text, with Arg1 and Arg2 defined semantically and the senses of discourse relations annotated besides connectives and their lexical alternatives.


It is worth noting that the majority of discourse relations in Chinese are implicit, so the insertion of a connective in an implicit position can significantly ease the understanding of the discourse.

a connective driven representation scheme is still applicable to a discourse with implicit connectives.


Given above the CDT scheme, we choose 500 Xinhua newswire documents from the Chinese Treebank (Xue et al., 2005b) in our Chinese Discourse Treebank (CDTB) annotation.

we annotate one discourse tree for each paragraph.


In the CDTB annotation, we employ a top-down strategy.

we determine the overall level first and then the analysis goes on step by step to the individual EDUs.


CDT takes advantage of both RST and PDTB, and well adapts to the special characteristics of Chinese discourse.

we describe CDT in detail from various perspectives, such as EDU, connective, structure, relation and nuclearity.


In general, learning the optimal F o prune , F o score pair can be intractable due to their potential interdependence.

when learning F prune in the worst case there can be ambiguity about which of the non-optimal actions to retain, and for only some of those an effective F score can be found.


The loss decomposition motivates a learning approach that targets minimizing the errors of pruning and scoring functions independently.

we optimize the overall loss of the Pruneand-Score approach in a stage-wise manner.


At each state s * i on the solution path of a training example (x, y * ), we create an example by labeling optimal action a * i ∈ A(s * i ) as the only relevant action, and then try to learn a ranking function that can rank actions such that the relevant action a * i is in the top b actions, where b is the input pruning paramter.

we have a rank learning problem, where the learner's goal is to optimize the Precision at Topb.


Game Representation We represent a game by the tuple H, A, T, R, Ψ , where H is the set of all possible game states, A = {(a, o)} is the set of all commands (action-object pairs), T (h | h, a, o) is the stochastic transition function between states and R(h, a, o) is the reward function.

the game state H is hidden from the player, who only receives a varying textual description, produced by a stochastic function Ψ : H → S. the underlying state h in the game engine keeps track of attributes such as the player's location, her health points, time of day, etc.